Andy Liaw's suggestion sounds sensible to me, though I don't have much experience with this kind of data either.
OTHER QUESTIONS:
* How big is J? I'm guessing it might be quite large, but I don't know.
* Are the spectra relatively smooth? I wonder if it might be appropriate to try to smooth the data some way preliminary to other analyses.
* How many observations do you have in each of "ill" and "healthy", especially relative to J?
I might try to do a principal components analysis (or "svd" if "princomp" bombed because of singular matrices) on the covariance matrix of the spectra. Then I might want to test how different the spectra were.
hope this helps. spencer graves
Liaw, Andy wrote:
Eh? The original message says it's the design matrix that is perfectly collinear after the transformation, not the response.
I don't know much about this type of data, but seems like you could just fit the model w/o intercept to eliminate the collinearity, no? It's the interpretation of the result that may be tricky, I think.
Andy
-----Original Message----- From: Spencer Graves [mailto:[EMAIL PROTECTED] Sent: Monday, June 02, 2003 9:33 AM To: Christoph Lehmann Cc: Spencer Graves; [EMAIL PROTECTED] Subject: Re: [R] compositional data: percent values sum up to 1
"glm" will do multinomial logistic regression. However, if J is large, I doubt if that will do what you want. If it were my problem, I might feel a need to read the code for "glm" and modify it to do what I want. Perhaps someone else can suggest something better.
hth. spencer graves
Christoph Lehmann wrote:
I want to do a logistic regression analysis, and to compare with, a
discriminant analysis. The mentioned power maps are my
exogenous data,
depends on manythe dependent variable (not mentioned so far) is a diagnosis (ill/healthy)
thanks for the interest and the help
Christoph
On Sun, 2003-06-01 at 21:01, Spencer Graves wrote:
What are you trying to do? What I would do with this
power data from Jfactors.
spencer graves
Christoph Lehmann wrote:
again, under another subject:
sorry, maybe an all too trivial question. But we have
of all ourfrequency spectra and to have the same range for the data
our x design-matrix,subjects, we just transformed them into % values, pseudo-code:
power[i,j]=power[i,j]/sum(power[i,1:J])
of course, now we have a perfect linear relationship in
data sum up tosince all power-values for each subject sum up to 1.
How shall we solve this problem: just eliminate one column of x, or
introduce a restriction which says exactly that our power
1 for each subject?
Thanks a lot
Christoph
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